Executive Forum:
The Evolving Financial Crimes Landscape

March 27, 2018
1:00pm – 6:30pm.

Join speakers from BMO, TD Bank, Accenture and more as we to explore current and future global trends in Financial Crimes. Designed for data and analytically driven leaders who are seeking innovative approaches to evolve their business, SAS Financial Crimes Executive Leadership as well as our customers and partners will be featured, highlighting best practices and the latest techniques leveraged to combat financial crime in the digital age across banking and insurance.

Agenda

1:00pm

Registration & Refreshments

Amanda Holden, Executive Lead, Financial Crimes, SAS

1:30pm

Welcome, Introduction & Overview

Cameron Dow, President of SAS Canada

1:40pm

AI and the Changing Face of Financial Crimes: Beyond the Marketing Hype
Is artificial intelligence clever enough for financial crimes and compliance? With so many vendors touting AI’s prophetic revolution, it’s tough to filter out the wheat from the chaff. In this session we’ll do just that as we discuss how SAS is practically and effectively enabling AI and machine learning (ML) initiatives in financial crimes around the world. We’ll share what we see coming as we look to the future, including how SAS is embedding AI & ML into its product roadmap.

The Impact of Digitization: Quick, Which is it? Approve, Review or BlockAs financial institutions increase the number of payment channels and variety of authentication methods, the resulting ecosystem is becoming increasingly challenging to maintain. In this session, we’ll cover industry trends and outliers as institutions work to maintain a balance of safety and customer experience in an ever growing landscape of real-time payments.

Case Studies on Next Generation AML: What is the Role of Artificial IntelligenceThis session focuses on where the rubber meets the road as we discuss case studies of financial institutions successfully leveraging AI technologies to address AML. We look at their struggles getting there, and things to consider along your transformational journey.

Global Trends in Insurance Fraud: Next Generation Network AnalyticsGlobal estimates indicate that between 10 to 15% of all personal lines insurance claims have some element of fraud. Learn how SAS supports over 90 insurers with advanced analytics solutions to prevent against fraud. Recent solutions leverage network based fraud detection to reduce false positives and drive greater insights and outcomes for the investigative teams.

The Modern FCIU: Special Risk InvestigationsAs the power of data and analytics grows, synergies across Financial Crimes (AML, Cyber, Fraud) are more and more apparent. Nowhere is this more evident than at the investigative level. Learn about approaches and tools to enable a high performing investigative unit to address high risk, special, multi-channel and complex investigations.

Suspect Behavior Identification through Sentiment Analysis & Communication SurveillanceDuring this session Accenture/SAS will demonstrate the power of deploying big data, advanced analytics and interactive visualizations, leading to threat identification and prudent risk decision-making.

Specifically, the demo will showcase the deployment of artificial intelligence and machine learning in the context of Surveillance Analytics, Social Network Analysis, and Deep Emotion / Sentiment Analysis, for detection of misconduct and behavioral risk in security, commercial and public sector environments.

Constantine Boyadjiev, Head of North American Fraud, Risk and Compliance Analytics, Accenture

The Convergence of Fraud, AML & Cyber and the Emergence of Enterprise Financial CrimesThe Financial Services industry has long deliberated the merits of converging the Fraud and AML organizations under a single umbrella, or at the very least, operating within a more collaborative ecosystem. Convergence can result in tremendous benefits, but many organizations remain cautious due to differences in organizational habits, mandates, priorities and sensitivities between the two departments. The devil is in the details as we discuss:

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A typical anti-money laundering (AML) transaction monitoring program has scenarios that monitor the customers and accounts that pose the most risk to the institution. The fact is … this one-size-fits-all methodology isn’t very effective. That’s because customers transact differently based on many factors. So how do you incorporate that into your program?

In addition to addressing risks through transaction monitoring, financial crimes investigation units (FCIUs) are expected to proactively identify financial crime risk, such as the firm’s exposure to geopolitical events and terrorism financing. It’s not just a matter of protecting the organization from regulatory and reputational risks, but also helping law enforcement combat serious national and global threats.

And if an incident were uncovered, investigators would need to be able to answer questions about who, what, where, when and why. Which parties, accounts and geographies are involved? What products are they using? What transaction trends are seen? Is this an ongoing or short-term risk? What caused it, and what actions are being taken?

Big data. Yeah … so what? What does big data have to do with insurers? Just think about it. You sift and search and sort incredible amounts of data – adjusters’ hand-written notes, data from fraud lists and the information from claims management systems and the NICB claims database. Are you getting the most from that insurance claims data?

Fraud detection is a challenging problem. The fact is that fraudulent transactions are rare; they represent a very small fraction of activity within an organization. The challenge is that a small percentage of activity can quickly turn into big dollar losses without the right tools and systems in place. Criminals are crafty. As traditional fraud schemes fail to pay off, fraudsters have learned to change their tactics. The good news is that with advances in machine learning, systems can learn, adapt and uncover emerging patterns for preventing fraud.

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